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Chen Qian
Researcher at SenseTime
Publications - 207
Citations - 10106
Chen Qian is an academic researcher from SenseTime. The author has contributed to research in topics: Computer science & Pose. The author has an hindex of 30, co-authored 125 publications receiving 5669 citations. Previous affiliations of Chen Qian include Shanghai Jiao Tong University & The Chinese University of Hong Kong.
Papers
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Proceedings ArticleDOI
Residual Attention Network for Image Classification
Fei Wang,Mengqing Jiang,Chen Qian,Shuo Yang,Cheng Li,Honggang Zhang,Xiaogang Wang,Xiaoou Tang +7 more
TL;DR: Residual Attention Network as mentioned in this paper is a convolutional neural network using attention mechanism which can incorporate with state-of-the-art feed forward network architecture in an end-to-end training fashion.
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Residual Attention Network for Image Classification
Fei Wang,Mengqing Jiang,Chen Qian,Shuo Yang,Cheng Li,Honggang Zhang,Xiaogang Wang,Xiaoou Tang +7 more
TL;DR: Residual Attention Network as discussed by the authors is a convolutional neural network using attention mechanism which can incorporate with state-of-the-art feed forward network architecture in an end-to-end training fashion.
Proceedings ArticleDOI
Realtime and Robust Hand Tracking from Depth
TL;DR: A hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy is proposed and presented, making it the first system that achieves such robustness, accuracy, and speed simultaneously.
Proceedings ArticleDOI
The Seventh Visual Object Tracking VOT2019 Challenge Results
Matej Kristan,Amanda Berg,Linyu Zheng,Litu Rout,Luc Van Gool,Luca Bertinetto,Martin Danelljan,Matteo Dunnhofer,Meng Ni,Min Young Kim,Ming Tang,Ming-Hsuan Yang,Abdelrahman Eldesokey,Naveen Paluru,Niki Martinel,Pengfei Xu,Pengfei Zhang,Pengkun Zheng,Pengyu Zhang,Philip H. S. Torr,Qi Zhang Qiang Wang,Qing Guo,Radu Timofte,Jani Käpylä,Rama Krishna Sai Subrahmanyam Gorthi,Richard M. Everson,Ruize Han,Ruohan Zhang,Shan You,Shaochuan Zhao,Shengwei Zhao,Shihu Li,Shikun Li,Shiming Ge,Gustavo Fernandez,Shuai Bai,Shuosen Guan,Tengfei Xing,Tianyang Xu,Tianyu Yang,Ting Zhang,Tomas Vojir,Wei Feng,Weiming Hu,Weizhao Wang,Abel Gonzalez-Garcia,Wenjie Tang,Wenjun Zeng,Wenyu Liu,Xi Chen,Xi Qiu,Xiang Bai,Xiaojun Wu,Xiaoyun Yang,Xier Chen,Xin Li,Alireza Memarmoghadam,Xing Sun,Xingyu Chen,Xinmei Tian,Xu Tang,Xue-Feng Zhu,Yan Huang,Yanan Chen,Yanchao Lian,Yang Gu,Yang Liu,Andong Lu,Chen Yanjie,Yi Zhang,Yinda Xu,Yingming Wang,Yingping Li,Yu Zhou,Yuan Dong,Yufei Xu,Yunhua Zhang,Yunkun Li,Anfeng He,Zeyu Wang Zhao Luo,Zhaoliang Zhang,Zhen-Hua Feng,Zhenyu He,Zhichao Song,Zhihao Chen,Zhipeng Zhang,Zhirong Wu,Zhiwei Xiong,Zhongjian Huang,Anton Varfolomieiev,Zhu Teng,Zihan Ni,Antoni Chan,Jiri Matas,Ardhendu Shekhar Tripathi,Arnold W. M. Smeulders,Bala Suraj Pedasingu,Bao Xin Chen,Baopeng Zhang,Baoyuan Wu,Bi Li,Bin He,Bin Yan,Bing Bai,Ales Leonardis,Bing Li,Bo Li,Byeong Hak Kim,Chao Ma,Chen Fang,Chen Qian,Cheng Chen,Chenglong Li,Chengquan Zhang,Chi-Yi Tsai,Michael Felsberg,Chong Luo,Christian Micheloni,Chunhui Zhang,Dacheng Tao,Deepak K. Gupta,Dejia Song,Dong Wang,Efstratios Gavves,Eunu Yi,Fahad Shahbaz Khan,Roman Pflugfelder,Fangyi Zhang,Fei Wang,Fei Zhao,George De Ath,Goutam Bhat,Guangqi Chen,Guangting Wang,Guoxuan Li,Hakan Cevikalp,Hao Du,Joni-Kristian Kamarainen,Haojie Zhao,Hasan Saribas,Ho Min Jung,Hongliang Bai,Hongyuan Yu,Houwen Peng,Huchuan Lu,Hui Li,Jiakun Li,Luka Čehovin Zajc,Jianhua Li,Jianlong Fu,Jie Chen,Jie Gao,Jie Zhao,Jin Tang,Jing Li,Jingjing Wu,Jingtuo Liu,Jinqiao Wang,Ondrej Drbohlav,Jinqing Qi,Jinyue Zhang,John K. Tsotsos,Jong Hyuk Lee,Joost van de Weijer,Josef Kittler,Jun Ha Lee,Junfei Zhuang,Kangkai Zhang,Kangkang Wang,Alan Lukezic,Kenan Dai,Lei Chen,Lei Liu,Leida Guo,Li Zhang,Liang Wang,Liangliang Wang,Lichao Zhang,Lijun Wang,Lijun Zhou +179 more
TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Proceedings ArticleDOI
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
TL;DR: Wu et al. as mentioned in this paper proposed a boundary-aware face alignment algorithm by utilizing boundary lines as the geometric structure of a human face to help facial landmark localisation, which achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin.